基于光学遥感和微波遥感的土壤水分反演模型研究

选用2020年5月17日Sentinel-1和Landsat 8影像数据,结合田间人工测墒数据,以辽宁省朝阳市为例,分别利用植被温度指数法和光学协同微波遥感反演算法反演土壤水分,构建高精度土壤水分预报模型。结果表明：基于光学遥感的植被温度指数(TVDI)不能较好反演农田土壤水分;微波反射能够较好反馈土壤水分的空间变化,Sentinel-1雷达数据VV极化对土壤水分的拟合精度(R²=0.71)优于VH极化(R²=0.27);基于全球植被水分指数(GVMI)改进的水云模型效果最优(R²=0.80)。利用微波和光学遥感协同反演,能够反演得到高空间分辨率、高精度的农田土壤水分数据,有助于农业干旱的监测。     

基于Sentinel-1 SAR数据的锡林浩特市典型草原土壤水分反演

土壤水分是影响植被生长发育的重要因子,为快速、有效了解典型草原植被生长状况及评估草原干旱灾害,以Sentinel-1合成孔径雷达(SAR)数据和地面实测数据为基础,利用雷达后向散射系数快速监测和反演锡林浩特市典型草原土壤水分并与地面实测数据进行对比。结果表明:SAR后向散射系数与实测土壤含水量存在显著相关关系,相关系数R~2达0.8;在不同地面粗糙度条件下,雷达后向散射系数与实测土壤水分相关系数达0.9。说明利用Sentinel-1 SAR数据能够快速、准确地对研究区进行土壤水分动态监测,这也将可能成为一种新的典型草原干旱监测方法。     

基于Sentinel-3条件植被温度指数的关中平原干旱监测

基于Sentinel-3数据计算多年条件植被温度指数（IVTC）,并进行近实时定量干旱监测,以陕西省关中平原为研究区,在单年Sentinel-3数据的基础上综合多年Terra MODIS数据计算多年Sentinel-3 IVTC,进而实现研究区的干旱监测。研究结果表明,基于单年Terra MODIS IVTC与多年Terra MODIS IVTC之间的线性回归模型计算关中平原的多年Sentinel-3 IVTC是可行的,并且计算的旬尺度多年Sentinel-3 IVTC与近20 d累积降水量具有较高的相关性,相关系数（R2）达到065,展现了较高的近实时定量干旱监测能力。     

基于ENVISAT/ASAR资料的土壤湿度反演方法

提出了利用ENVISAT/ASAR/AP模式VV极化的低入射角数据和黄土高原陆面过程试验资料对黄土高原平凉试验区表层土壤湿度进行反演的方法。结果表明:在相对平坦的混合地表类型区域,反演的土壤湿度与地面实测值平均误差<0.02 cm3.cm-3,绝对误差均在0.04 cm3.cm-3范围内,该结果能较好地对黄土高原塬区土壤湿度进行干旱监测;但在陡峭山坡及塬边等地形起伏较大的区域,结果较差。     

基于交叉极化数据的海面风速反演研究

全极化数据可以获取比单极化数据更多的目标信息,研究发现C波段交叉极化数据同样可用于海面风速反演。针对RADARSAT-2 Fine Quad模式具有全极化成像的特点,以我国东部海域为研究区,结合同极化数据和交叉极化数据反演海面风速模型,探究各种极化数据的最优风速反演方法。对于同极化数据采用地球物理模型函数(GMF)和极化率模型(PR)组合的方式进行海面风速反演,对交叉极化数据采用C波段交叉极化海面散射模型(C-2PO)进行海面风速反演,反演结果与ERA-Interim风场数据进行比较分析;此外,对Scan SAR模式交叉极化数据的后向散射系数与海面风速的关系进行探索分析。研究结果表明,RADARSAT-2 Fine Quad模式四种极化数据选用合适的模型均可反演出高精度的海面风速,其中VH和HV极化数据的反演结果基本相同,交叉极化数据反演风速效果好于同极化数据,同时,Scan SAR模式交叉极化数据的后向散射系数随海面风速的增大表现出一定的线性变化趋势。全极化模式数据在海面风速反演上表现出比单极化模式数据较为明显的优势,将成为未来海面风速反演的发展方向。     

利用温度植被干旱指数反演伊犁博州地区土壤湿度

利用MODIS合成产品数据MOD11A2和MOD13A2获取的归一化植被指数（NDVI）和陆地表面温度（LST）构建Ts—NDVI特征空间,并把该特征空间计算的温度植被干旱指数（TVDI）作为土壤湿度指标。利用该指标反演伊犁博州地区6～8月3个月份每8d的土壤湿度。然后将土壤湿度分为5级,进而得到该时段伊犁博州地区土壤湿度空间分布特征。     

卫星被动微波遥感土壤湿度研究进展

土壤湿度是控制陆地和大气间水分和能量交换过程的重要变量,而被动微波遥感是众多监测土壤湿度技术中最有效的手段之一。文中概述了被动微波反演土壤湿度的物理原理,重点介绍了被动微波反演土壤湿度的主要模型。在对不同模型进行比较分析后,基于不同传感器类型分别列举了当今发展较完善的3个典型算法：①Njoku和Li基于AMSR的多通道同时反演土壤湿度、土壤温度、植被含水量的方法;②Owe等基于SMMR利用极化差异指数同时反演土壤湿度和植被光学厚度2个参数的方法;③Wen等基于SSM／I同时反演土壤湿度和土壤温度的方法。对被动微波遥感土壤湿度研究中目前所存在的问题和发展前景进行了一些探讨。     

基于Radarsat-2 SAR数据反演定西裸露地表土壤水分

利用Radarsat-2 SAR数据和定西地区野外土钻法及WET仪器观测的土壤水分数据,分析了同极化后向散射系数与不同土层深度土壤水分之间的关系,采用交叉极化(VV/VH)组合模型反演土壤水分并进行对比验证。结果表明:水平、垂直同极化后向散射系数均与10~20 cm土壤含水量相关性最好,相关系数R均为0.74;受地表粗糙度和土壤质地等影响,同极化后向散射系数与0~10 cm土壤水分相关性均较低。交叉极化组合模型的反演值与10~20 cm实测土壤水分相关性较高,R值达0.75,而与0~10 cm和20~30 cm实测值的相关性较低(R值分别为0.47和0.52),但均通过α=0.05的显著性检验;WET仪器实测0~6 cm土壤水分经校正后与反演值的相关系数为0.46(通过α=0.01的显著性检验),校正后的结果有效提高了WET仪器测量精度。交叉极化组合模型可用于裸露地表土壤水分的反演,更适用于提取10~20 cm土壤含水量信息。     

基于MonoRTM模型的微波辐射计反演方法研究

基于辐射传输模型MonoRTM计算天空亮温度,使用多元线性统计回归方法和BP神经网络方法分别对大气温度和水汽密度廓线进行了反演,检验并分析了两种方法的反演精度.结果表明,多元线性回归方法反演的温度偏差总体不大于6K,反演的水汽密度偏差小于4 g/m3;神经网络方法反演的温度偏差小于2K,反演的水汽密度误差总体不大于2 g/m3.与探空数据的对比表明,对于大气温度和水汽密度反演,BP神经网络方法的反演结果都要比多元线性回归方法的反演结果更接近探空资料值.     

基于CyGNSS数据的土壤水分与植被光学厚度反演研究

本文提出了一种仅基于CyGNSS数据,能够同时反演土壤水分与植被光学厚度的方案,该方案使用了神经网络与暴力穷举算法.首先考察了2018年以及2020年的数据,并对结果进行了验证.通过分析发现反演结果与参考数据展现了良好的一致性.土壤水分的反演结果与2018年和2020年的测试数据比较,其相关系数分别高达0.86和0.84,均方根误差分别为0.064和0.071 cm³/cm³;对于植被光学厚度,2018年与2020年的相关系数均为0.98,均方根误差分别为0.079和0.084.研究结果表明,CyGNSS可作为一种新型且独立的泛热带土壤水分与植被光学厚度反演手段.     

Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations

Summary A radiative transfer model has been used to determine the large scale effective 6.6 GHz and 37 GHz optical depths of the vegetation cover. Knowledge of the vegetation optical depth is important for satellite-based large scale soil moisture monitoring using microwave radiometry. The study is based on actual observed large scale surface soil moisture data and observed dual polarization 6.6 and 37 GHz Nimbus/SMMR brightness temperatures over a 3-year period. The derived optical depths have been compared with microwave polarization differences and polarization ratios in both frequencies and with Normalized Difference Vegetation Index (NDVI) values from NOAA/AVHRR. A synergistic approach to derive surface soil emissivity from satellite observed brightness temperatures by inverse modelling is described. This approach improves the relationship between satellite derived surface emissivity and large scale top soil moisture fromR ²=0.45 (no correction for vegetation) toR ²=0.72 (after correction for vegetation). This study also confirms the relationship between the microwave-based MPDI and NDVI earlier described and explained in the literature.List of Symbols f frequency [Hz] - f _i(p) fractional absorption at polarizationp - h surface roughness - h h cos² - H horizontal polarization - n _i complex index of refraction - p polarization (H orV) - R _s microwave surface reflectivity - T _B(p) brightness temperature at polarizationp - T ^* normalized brightness temperature - T polarization difference (T _v-T _H) - T _s temperature of soil surface - T _c temperature of canopy - T _max daily maximum air temperature - T _min daily minimum air temperature - V vertical polarization - soil moisture distribution factor; also used for the constant to partition the influence of bound and free water components to the dielectric constant of the mixture - empirical complex constant related to soil texture - microwave transmissivity of vegetation (=e ^–) - ^* effective transmissivity of vegetation (assuming =0) - microwave emissivity - _s emissivity of smooth soil surface - _rs emissivity of rough soil surface - _vs emissivity of vegetated surface - soil moisture content (% vol.) - K dielectric constant [F·m^–1] - K _fw dielectric constant of free water [F·m^–1] - K _ss dielectric constant of soil solids [F·m^–1] - K _m dielectric constant of mixture [F·m^–1] - K _o permittivity of free space [8.854·10^–12 F·m^–1] - high frequency limit ofK _wf [F·m^–1] - wavelength [m] - incidence angle [degrees from nadir] - polarization ratio (T _H/T _V) - b soil bulk density [gr·cm^–3] - s soil particle density [gr·cm^–3] - R surface reflectivity in red portion of spectrum - NIR surface reflectivity in near infrared portion of spectrum - _eff effective conductivity of soil extract [mS·cm^–1] - vegetation optical depth - _6.6 vegetation optical depth at 6.6 GHz - ₃₇ vegetation optical depth at 37 GHz - ^* effective vegetation optical depth (assuming =0) - single scattering albedo of vegetation With 12 Figures     

LPJ-WHyMe模型对1997～2010年中国东北地区潜在植被分布和碳循环的模拟研究

利用一套高分辨率的气候驱动场和全球动态植被模型LPJ-WHyMe(Lund-Potsdam-Jena-Wetland Hydrology and Methane),模拟了中国东北地区潜在植被分布,并对中国东北地区1997～2010年平均净初级生产力（Net Primary Production, NPP）、净生态系统生产力（Net Ecosystem Production, NEP）、燃烧面积、火灾碳排放、土壤温度和土壤湿度进行了估算。LPJ-WHyMe的特点在于能够描述冻融的物理过程以及土壤中多层的湿度和温度。数值结果表明,在LPJ-WHyMe模型提供的植被功能类型（Plant Function Type, PFT）划分的条件下,中国东北地区主要分布了5种植被功能类型,即温带夏绿阔叶林带、北方常绿针叶林带、北方夏绿针叶林带、北方夏绿阔叶林带和温带草本植物。在研究时间段内,中国东北地区NPP的年平均值为376 g(C) m-2,变化范围在324.15～424.86 g(C) m-2之间。火灾机制的引入使得LPJ-WHyMe模型对NEP的模拟能力进一步提高,即NEP年平均值为42.36 g(C) m-2,表明中国东北地区陆地生态系统总体表现为“碳汇”。中国东北地区年平均燃烧面积分数为0.84%,火灾碳排放量为42.41 g(C) m-2,整体上模型高估了燃烧面积值和火灾碳排放量,模型对东北地区火灾的模拟仍然存在一定的局限性。中国东北地区土壤温度与气温呈正相关关系,且各层土壤温度与气温的相关性随着深度的增加而减弱。中国东北地区土壤湿度与降水呈正相关关系,土壤湿度与气温呈反相关关系。上述结果表明LPJ-WHyMe模型模拟中国东北地区潜在植被分布和碳循环是有效的。     

Detection of Anthropogenic CO2 Emission Signatures with TanSat CO2 and with Copernicus Sentinel-5 Precursor (S5P) NO2 Measurements: First Results

China’s first carbon dioxide(CO₂) measurement satellite mission, TanSat, was launched in December 2016. This paper introduces the first attempt to detect anthropogenic CO₂ emission signatures using CO₂ observations from TanSat and NO₂ measurements from the TROPOspheric Monitoring Instrument(TROPOMI) onboard the Copernicus Sentinel-5 Precursor(S5P) satellite. We focus our analysis on two selected cases in Tangshan, China and Tokyo, Japan.We found that t...     

The potential of vegetation feedback to alleviate climate aridity over the United States associated with a 2×CO2 climate condition

This study examines the potential impact of vegetation feedback on changes in summer climate aridity over the contiguous United States (US) due to the doubling of atmospheric CO₂ concentration using a set of 100-year-long climate simulations made by a global climate model interactively coupled with a dynamic vegetation model. The Thornthwaite moisture index (I _m ), which quantifies climate aridity on the basis of atmospheric water supply (i.e., precipitation) and atmospheric water demand (i.e., potential evapotranspiration, PET), is used to measure climate aridity. Warmer atmosphere and drier surface resulting from increased CO₂ concentration increase climate aridity over most of the contiguous US. This phenomenon is due to larger increments in PET than in precipitation, regardless of the presence or absence of vegetation feedback. Compared to simulations without active dynamic vegetation feedback, the presence of vegetation feedback significantly alleviates the increase in aridity. This vegetation-feedback effect is most noticeable in the subhumid regions such as southern, midwestern and northwestern US, primarily by the increasing vegetation greenness. In these regions, the greening in response to warmer temperatures enhances moisture transfer from soil to atmosphere by evapotranspiration (ET). The increased ET and subsequent moistening over land areas result in weaker surface warming (1–2?K) and PET (3–10?mm?month^?1), and greater precipitation (4–10?mm?month^?1). Collectively, they result in moderate increases in I _m . Our results suggest that moistening by enhanced vegetation feedback may prevent aridification under climatic warming especially in areas vulnerable to climate change, with consequent implications for mitigation strategies.     

Raindrop Size Distribution Parameters Retrieved from Guangzhou S-band Polarimetric Radar Observations

According to the statistical shape–slope (μ–Λ) relationship observed for the first time by several 2D-Video-Distrometers (2DVD) in southern China, a constrained gamma (C-G) model was proposed for the retrieval of rain drop size distributions (DSDs) from Guangzhou S-band polarimetric radar observations. Two typical precipitation processes were selected to verify the accuracy of the retrieval scheme. The μ–Λ relationship: Λ = 0.0241μ² + 0.867μ + 2.453 was obtained based on the 2DVD observation results from at Huizhou Longmen station, which is a very representative location in the area. Relying on the Guangzhou polarimetric radar measurements of radar reflectivity (Z_HH) and differential reflectivity (Z_DR), the gamma (Γ) size distribution parameters (N₀, μ, and Λ) can be retrieved by the C-G model retrieval scheme. The results show that the Guangzhou polarimetric radar retrievals of DSDs were close to the 2DVD observations at Guangzhou Maofengshan station. The rain rate, mass mean diameter, and normalized intercept parameter of radar retrievals were in good agreement with the 2DVD observations, and the relative errors were less than 10%. The overall accuracy of the retrieval scheme was high. The retrieval scheme has established the relationship between the polarimetric radar measurements and gamma size distribution parameters. It will be helpful to in-depth research and application of the dual-polarization radar data in microphysical precipitation processes analysis, as well as convection-resolved numerical model data assimilation and prediction effect evaluation.     

A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager(MWRI)

Accurate measurements of soil moisture are beneficial to our understanding of hydrological processes in the earth system. A multivariable approach using the random forest(RF) machine learning technique is proposed to estimate the soil moisture from Microwave Radiation Imager(MWRI) onboard Fengyun-3 C satellite. In this study, Soil Moisture Operational Products System(SMOPS) products disseminated from NOAA are used as a truth to train the algorithm with the input of MWRI brightness temperatures(TBs) at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, TB polarization ratios(PRs) at 10.65, 18.7, and 23.8 GHz, height in digital elevation model(DEM), and soil porosity. The retrieved soil moisture is also validated against the independent SMOPS data, and the correlation coefficient is about0.8 and mean bias is 0.002 m3 m-3 over the period from 1 August 2017 to 31 May 2019. Our retrieval of soil moisture also has a higher correlation with ECMWF ERA5 soil moisture data than the MWRI operational products. In the western part of China, the spatial distribution of MWRI soil moisture is much improved, compared to the MWRI operational products.     

四套土壤湿度再分析数据在中国西北东部—华北—江淮地区的适用性研究

基于1992~2010年全国778个农业气象站土壤湿度观测资料、ERA-Interim、JRA55、NCEP-DOE R2和20CR土壤湿度再分析资料,通过平均差值、相关系数、差值标准差、标准差比四个参数,利用Brunke排名方法和EOF(Empirical Orthogonal Function)分析,对四套土壤湿度再分析资料在中国西北东部—华北—江淮区域的适用性进行了分析。主要结论如下:不同季节的平均偏差空间分布上,JRA55资料同观测数据的平均偏差在±0.08m~3 m~(-3)之间,春、夏季西北东部JRA55土壤湿度偏小,ERA-Interim、NCEP-DOE R2、20CR资料较观测数据偏湿,华北南部、江淮地区平均偏差小于西北东部、华北北部。在年际变化上,各个季节ERA-Interim资料同观测资料最为接近,能稳定地再现西北东部、华北、江淮地区土壤湿度干湿变化趋势,反映出重要的旱涝年。整体而言,四套再分析资料中ERA-Interim资料同观测资料接近,JRA55、NCEP-DOE R2资料次之,20CR资料最差。     

CLDAS土壤湿度模拟结果及评估

中国气象局陆面数据同化系统（CLDAS V1.0）由陆面驱动数据融合和陆面模式模拟两部分组成。基于驱动数据,选取Canmunity Land Model 3.5（CLM3.5）作为CLDAS V1.0系统的陆面模式进行模拟试验,并对土壤模拟结果进行评估。利用2013年经过质量控制的中国气象局业务化自动土壤水分观测站实况数据、青藏高原试验观测数据及国际同类产品对模拟结果进行评估,结果表明:从各省以及全国平均结果看,相关系数普遍在0.8以上,偏差基本为-0.04~0.04 mm3·mm-3,平均均方根误差为0.04~0.05 mm3·mm-3,在青藏高原地区与国际同类产品相比,精度也有一定提高。总体而言,模拟结果已达到较高精度,数据集产品对中国区域干旱监测等具有重要意义。     

The Asian Dust Aerosol Model 2 (ADAM2) with the use of Normalized Difference Vegetation Index (NDVI) obtained from the Spot4/vegetation data

The operational Asian Dust Aerosol Model (ADAM)1 in Korea Meteorological Administration has been modified to the ADAM2 model to be used as an operational forecasting model all year round not only in Korea but also in the whole Asian domain (70-160°E and 5-60°N) using the routinely available World Meteorological Organization (WMO) surface reporting data and the Spot/vegetation Normalized Difference Vegetation Index (NDVI) data for the period of 9 years from 1998 to 2006. The 3-hourly reporting WMO surface data in the Asian domain have been used to re-delineate the Asian dust source region and to determine the temporal variation of the threshold wind speed for the dust rise. The dust emission reduction factor due to vegetation in different surface soil-type regions (Gobi, sand, loess, and mixed soil) has been determined with the use of NDVI data. It is found that the threshold wind speed for the dust rise varies significantly with time (minimum in summer and maximum in winter) and surface soil types with the highest threshold wind speed of 8.0 m?s^?1 in the Gobi region and the lowest value of 6.0 m?s^?1 in the loess region. The statistical analysis of the spot/vegetation NDVI data enables to determine the emission reduction factor due to vegetation with the free NDVI value that is the NDVI value without the effect of vegetation and the upper limit value of NDVI for the dust rise in different soil-type regions. The modified ADAM2 model has been implemented to simulate two Asian dust events observed in Korea for the periods from 31 March to 2 April 2007 (a spring dust event) and from 29 to 31 December 2007 (a winter dust event) when the observed PM₁₀ concentration at some monitoring sites in the source region exceeds 9,000 μg m^?3. It is found that ADAM2 model successfully simulates the observed high dust concentrations of more than 8,000 μg m^?3 in the dust source region and 600 μg m^?3 in the downstream region of Korea. This suggests that ADAM2 has a great potential for the use of an operational Asian dust forecast model in the Asian domain.